The Oxford Training Centre‘s Keras for Deep Learning course provides an in-depth introduction to Keras, one of the most popular deep learning frameworks accessible today. This course is intended for both beginners and expert learners who are eager to explore the realm of deep learning. Keras, which is based on TensorFlow, provides an easy-to-use interface for creating, training, and deploying deep learning models, making it a powerful tool for AI and machine learning.
The Keras deep learning tutorial included in this course will walk you through all of the fundamental principles and practical techniques required to properly build neural networks using Keras. You will learn how to use the Keras machine learning library to create, train, and evaluate deep learning models. This course will also explore Keras for AI applications, including real-world examples of Keras deep learning models applied in several industries.
Objectives and Target Group
Objectives
By the end of this course, participants will be able to:
- Understand basic concepts of Keras for beginners and advanced Keras in deep learning.
- Learn to build Keras models through designing, training, and validating neural networks.
- Get familiar with examples of Keras deep learning models applied to solve real-world AI challenges.
- Work with deep learning models using Keras, including various types of neural networks.
- Learn Keras for machine learning and understand how to apply machine learning through deep learning techniques.
- Use Keras and TensorFlow for deep learning to create scalable AI models.
- Deploy and optimize Keras Python deep learning models for production environments.
- Build and tune Keras neural networks using best practices from a range of AI applications.
- Critically apply Keras in modeling real-world deep learning solutions to complex issues across various topics.
Target Group
This course is ideal for:
- Beginners who have an interest in learning deep learning and AI, and want to get started with Keras for AI applications.
- Data scientists and machine learning practitioners looking to enhance their skills in Keras for machine learning and understand how to use deep learning frameworks effectively.
- AI enthusiasts who are eager to gain experience with the Keras deep learning framework and explore the field of deep learning with Keras.
- Software developers who want to integrate deep learning models into their applications and learn to build neural networks with Keras.
- Researchers and engineers working on advanced deep learning projects and seeking to improve their knowledge in Keras for advanced deep learning.
- Professionals in industries such as healthcare, finance, and technology who want to apply Keras for deep learning to solve business and research problems.
Course Content
This course is structured to provide a comprehensive and practical approach to building deep learning models using Keras. It includes detailed lessons on the following topics:
1. Introduction to Keras and Deep Learning
- Understanding deep learning and its applications in machine learning.
- Introduction to the Keras deep learning framework and its advantages for building neural networks.
- Overview of TensorFlow and how Keras interacts with it.
- Setting up the environment for Keras Python deep learning.
2. Building Your First Neural Network with Keras
- Introduction to neural networks and the building blocks of Keras neural networks.
- Step-by-step guide to building a simple neural network using Keras for beginners.
- Understanding layers, activation functions, and compiling models.
- Hands-on exercises to build and train a basic deep learning model.
3. Advanced Techniques in Keras
- Working with advanced Keras deep learning models examples.
- Implementing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).
- Techniques for handling image, text, and time-series data with Keras for AI applications.
- Using Keras for advanced deep learning tasks like image recognition and natural language processing.
4. Optimizing and Evaluating Models
- Techniques for fine-tuning and optimizing Keras deep learning models.
- Model evaluation metrics: accuracy, precision, recall, and F1-score.
- Regularization and dropout methods to prevent overfitting.
- Hyperparameter tuning and using grid search with Keras.
5. Working with Complex Datasets
- Techniques for data preprocessing and augmentation using Keras for machine learning.
- Handling large datasets with batch processing.
- Introduction to transfer learning and pre-trained models.
- Applying Keras deep learning tutorial to complex datasets.
6. Deploying Deep Learning Models
- Exporting models and saving them for future use.
- Creating web applications with deep learning models using Flask or Django.
- Integration of Keras models into production environments.
- Overview of deploying Keras for machine learning models in the cloud.
7. Deep Learning with Keras and TensorFlow
- A deeper dive into using Keras for deep learning with TensorFlow as the backend.
- Benefits of TensorFlow and Keras together for building scalable AI systems.
- Example projects using deep learning with Keras and TensorFlow.